Search Results for author:
Found 3 papers, 0 papers with code
Learning-Based and Quality Preserving Super-Resolution of Noisy Images
The experimental results show that our method outperforms learning-based methods, has comparable results with standard methods, preserves the properties of the input image as contours, brightness, and textures, and reduces the artefacts.
Learning-based Framework for US Signals Super-resolution
We qualitatively and quantitatively test our model on different anatomical districts (e. g., cardiac, obstetric) images and with different up-sampling resolutions (i. e., 2X, 4X).
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images
We define a novel deep learning framework for the real-time denoising of ultrasound images.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
